Genome-Wide Association Analysis Identifies Eleven Risk Variants Associated With

Genome-wide association analysis identifies eleven risk variants associated with asthma-

with-hayfever phenotype

ONLINE REPOSITORY

Manuel A.R. Ferreira, PhD,a* Melanie C. Matheson, PhD,b* Clara S. Tang, PhD,a,c Raquel

Granell, PhD,d Wei Ang, PhD,e Jennie Hui, PhD,f-i Amy K. Kiefer, PhD,j David L. Duffy,

PhD,a Svetlana Baltic, PhD,k Patrick Danoy, PhD,l Minh Bui, PhD,b Loren Price, PhD,k Peter

D. Sly, FRACP,m Nicholas Eriksson, PhD,j Pamela A. Madden, PhD,n Michael J. Abramson,

PhD,o Patrick G. Holt, FRCP,p Andrew C. Heath, DPhil,n Michael Hunter, PhD,g,i Bill Musk,

FRACP, g,i,q,r, AAGC collaboratorss, Colin F. Robertson, FRACP,t Peter Le Souëf, FRACP,u

Grant W. Montgomery, PhD,a John A. Henderson, PhD,d Joyce Y. Tung, PhD,j Shyamali C.

Dharmage, PhD,b Matthew A. Brown, FRACP,l Alan James, FRACP,i,q,v Philip J. Thompson,

FRACP,k Craig Pennell, PhD,e Nicholas G. Martin, PhD,a David M. Evans, PhD,d,w David A.

Hinds, PhD,j and John L. Hopper, PhD,b Supplementary Methods

Genotyping, quality control (QC) and imputation procedures

AAGC. All 2,137 samples were genotyped with the Illumina 610K array. SNPs were excluded from analysis if the call rate was <95%, minor allele frequency (MAF) < 0.01 and Hardy-

Weinberg equilibrium test P-value < 10-6. SNPs passing QC were then used to impute with

Impute2E1 5.7 million variants with a MAF ≥ 0.01 and information > 0.3 using the combined

1000 Genomes Project and HapMap 3 phased data (all ancestral groups) as reference panels.

X chromosome SNPs (N =140,388 with MAF ≥ 0.01) were imputed with r2 > 0.3 with

MACH/minimacE2, 3 using the 1000G Phase I Integrated Release Version 3 (N = 584

European haplotypes) release of the 1000 Genomes Project.E4 All subjects were confirmed to be unrelated and of European ancestry through the analysis of genome-wide allele sharing.

23andMe. DNA extraction and genotyping were performed by the National Genetics

Institute (NGI), a CLIA-certified clinical laboratory and subsidiary of Laboratory

Corporation of America. Samples were genotyped on one of two platforms. About 35% of the participants were genotyped using the Illumina HumanHap550+ BeadChip, which included

SNPs from the standard HumanHap550 panel augmented with a custom set of approximately

25,000 SNPs selected by 23andMe. Two slightly different versions of this platform were used, as previously described.E5 The remaining 65% of participants were genotyped using the

Illumina HumanOmniExpress+ Bead Chip. This has a base set of 730,000 SNPs, augmented with approximately 250,000 SNPs to obtain a superset of the HumanHap550+ content, as well as a custom set of about 30,000 SNPs. Every sample that failed to reach a 98.5% call rate for SNPs on the standard platforms was re-analyzed. Persons included in the analysis were selected for having >97% European ancestry, as determined through an analysis of local ancestry via comparison to the three HapMap 2 populations.E6 A maximal set of unrelated individuals was chosen for the analysis using a segmental identity-by-descent (IBD) estimation algorithm.E7 Individuals were defined as related if they shared more than 700 cM

IBD, including regions where the two individuals share either one or both genomic segments

IBD. This level of relatedness (roughly 20% of the genome) corresponds approximately to the minimal expected sharing between first cousins in an outbred population. Participant genotype data were imputed against the August 2010 release of 1000 Genomes reference haplotypes.E8 First, we used BeagleE9 (version 3.3.1) to phase batches of 8,000-9,000 individuals across chromosomal segments of no more than 10,000 genotyped SNPs, with overlaps of 200 SNPs. We excluded SNPs with MAF < 0.001, Hardy-Weinberg equilibrium

P < 10−20, call rate < 95%, or with large allele frequency discrepancies compared to the 1000

Genomes reference data. We then assembled full phased chromosomes by matching the phase of haplotypes across the overlapping segments. We imputed each batch against the European subset of 1000 Genomes haplotypes using minimac,E10 using 5 rounds and 200 states for parameter estimation. Analyses were limited to 7.4 million SNPs with imputed r2 > 0.5 averaged across all batches, and r2 > 0.3 in every batch. For the non-pseudoautosomal region of the X chromosome, males and females were phased together in segments, treating the males as already phased; the pseudoautosomal regions were phased separately. We assembled fully phased X chromosomes, representing males as homozygous pseudo-diploids for the non-pseudoautosomal region. We then imputed males and females together using minimac as with the autosomes.

ALSPAC . A total of 9,912 children were genotyped using the Illumina HumanHap550 quad array by 23andMe subcontracting the Wellcome Trust Sanger Institute, Cambridge, UK and the NGI, USA. Individuals were excluded from further analysis on the basis of having incorrect gender assignments; minimal or excessive heterozygosity (<0.320 and >0.345 for the Sanger data and <0.310 and >0.330 for the LabCorp data); disproportionate levels of individual missingness (>3%); evidence of cryptic relatedness (>10% IBD) and being of non-

European ancestry (as detected by a multidimensional scaling analysis seeded with HapMap

2 individuals). The resulting data set consisted of 8,365 children. SNPs with a MAF of < 0.01 and call rate of < 95% were removed. Furthermore, only SNPs which passed an exact test of

Hardy Weinberg equilibrium (P > 5 × 10-7) were considered for analysis. After cleaning,

500,527 directly genotyped SNPs were available for analysis. HapMap variants were imputed with MACH 1.0.16 Markov Chain Haplotyping software,E2, 3 using CEPH individuals from phase 2 of the HapMap project as a reference set (release 22).

Raine . All 1,494 samples were genotyped using the Illumina 660K array. SNPs were excluded from analysis if the call rate was <95%, MAF < 0.01 and Hardy-Weinberg equilibrium test P-value < 5.7x10-7. SNPs passing QC were then used to impute HapMap

SNPs with MACH v1.0.16.E2, 3 Only SNPs with imputed r2 > 0.3 were retained for analysis.

All subjects were confirmed to be unrelated and of Caucasian ancestry through the analysis of genome-wide allele sharing.

Association analyses

AAGC. Autosomal SNPs were tested for association with case-control status using an allelic test of association implemented in PLINK;E11 for imputed variants, we analysed best-guess genotypes. X chromosome SNPs were analysed with logistic regression under an additive model (dosage for imputed variants) using MACH2DATE2, 3 and assuming a dosage compensation model, ie. equating hemizygous males to homozygous females,E12 such that the allelic dosage extremes for males were 0 (if A/-, as for AA females) and 2 (if B/-, as for BB females).

23andMe. Genome-wide association analyses of asthma-with-hayfever were performed using custom software. We computed likelihood ratio tests for an additive effect of allelic dosage using logistic regression with covariates for age, gender, and five principal components to account for residual population substructure. The X chromosome was treated the same as the autosomes; allele dosages ranged from 0 to 2 for both men and women, as for the AAGC analysis.

ALSPAC . Genome-wide association analysis of asthma-with-hayfever was carried out using MACH2DATE2, 3 by regressing expected allelic dosage on case-control status and assuming an additive model.

Raine . Autosomal SNPs were analysed with logistic regression under an additive model (dosage for imputed variants) using ProbABEL.E13 E References

E1. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009; 5:e1000529. E2. Li Y, Willer C, Sanna S, Abecasis G. Genotype imputation. Annu Rev Genomics Hum Genet 2009; 10:387-406. E3. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010; 34:816-34. E4. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491:56-65. E5. Eriksson N, Macpherson JM, Tung JY, Hon LS, Naughton B, Saxonov S, et al. Web- based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet 2010; 6:e1000993. E6. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 2003; 164:1567-87. E7. Henn BM, Hon L, Macpherson JM, Eriksson N, Saxonov S, Pe'er I, et al. Cryptic distant relatives are common in both isolated and cosmopolitan genetic samples. PLoS One 2012; 7:e34267. E8. Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, et al. A map of human genome variation from population-scale sequencing. Nature 2010; 467:1061-73. E9. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet 2007; 81:1084-97. E10. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 2012; 44:955-9. E11. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81:559-75. E12. Clayton D. Testing for association on the X chromosome. Biostatistics 2008; 9:593- 600. E13. Aulchenko YS, Struchalin MV, van Duijn CM. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics 2010; 11:134. E14. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E, Heath S, et al. A large- scale, consortium-based genomewide association study of asthma. N Engl J Med 2010; 363:1211-21. E15. Ferreira MA, Hottenga JJ, Warrington NM, Medland SE, Willemsen G, Lawrence RW, et al. Sequence variants in three loci influence monocyte counts and erythrocyte volume. Am J Hum Genet 2009; 85:745-9. E16. Ferreira MA, Mangino M, Brumme CJ, Zhao ZZ, Medland SE, Wright MJ, et al. Quantitative trait loci for CD4:CD8 lymphocyte ratio are associated with risk of type 1 diabetes and HIV-1 immune control. Am J Hum Genet 2010; 86:88-92. E17. Paternoster L, Standl M, Chen CM, Ramasamy A, Bonnelykke K, Duijts L, et al. Meta-analysis of genome-wide association studies identifies three new risk loci for atopic dermatitis. Nat Genet 2012; 44:187-92. E18. Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet 2007; 39:857-64. E19. Ramasamy A, Curjuric I, Coin LJ, Kumar A, McArdle WL, Imboden M, et al. A genome-wide meta-analysis of genetic variants associated with allergic rhinitis and grass sensitization and their interaction with birth order. J Allergy Clin Immunol 2011; 128:996-1005. E20. Sawcer S, Hellenthal G, Pirinen M, Spencer CC, Patsopoulos NA, Moutsianas L, et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 2011; 476:214-9. E21. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 2010; 26:2336-7. E Tables

Table E1. Participants included in the GWAS of asthma-with-hayfever.

Cases Controls

Sex, Age Sex, Cohort Age, mean Asthma age-of-onset, N (%) N N N female (mean, range) N female (%) (range) (%) ≤16 >16 Unknown AAGC 1,505 41 (4 - 89) 923 (61) 772 (51) 462 (31) 271 (18) 632 37 (12 - 91) 379 (60) 23andMe 4,230 48 (6-102) 2,272 (54) 2,329 (55) 1,898 (45) 3 (0) 10,842 49 (2-97) 4,180 (39) ALSPAC 668 14 (14-14) 283 (42) 668 (100) 0 (0) 0 (0) 2,132 14 (14-14) 1,118 (52) Raine 282 17 (16-18) 127 (45) 274 (97) 8 (3) 0 (0) 485 17 (16-18) 247 (51) Total 6,685 4,055 (61) 4,043 (61) 2,368 (35) 274 (4) 14,091 5,924 (42) Table E2. Screening items used to classify hayfever status.

N N Question used to classify Cohort Study Sub-study Cases Controls cases controls hayfever status AAGC QIM ADOL 81 280 Has a doctor ever diagnosed “Yes” “No” R you as suffering from hayfever? ASTHMA 346 0 Have you EVER had hayfever “Yes” “No” or nasal allergies? ALC1 0 118 How often have you had Hay “Sometimes” or “Never”, before fever? [before & after age 14] “Often”, before or & after age 14 after age 14 CANB 0 25 How often have you had Hay “Only as a child” “Never” fever? or “Quite often” ECZEMA 71 0 Has a doctor ever diagnosed “Yes” “No” you as suffering from Hayfever? LIWA 438 37 How often have you had Hay “Yes” “No” fever? TAHS 296 0 Have you ever had “Yes” “No” hayfever( that is sneezing running or blocked nose when you do not have a cold or the flu)? BUSS 273 172 Q1: Has your doctor ever told “Yes” to Q1 “No” to Q1 & you that you had Hayfever? Q2 & Q3 & Q4 Q2: Do you sneeze or get an itchy running nose? Q3: Has your chest ever made a wheezing or whistling sound? Q4: Have you ever felt tight in the chest? Raine 282 485 Has anyone ever told you that “Paediatrician, “Not diagnosed your child has hayfever? specialist or by doctor diagnosed” paediatrician, specialist or doctor” ALSPAC 668 2,132 Q1: Has he/she had hay fever “Yes” to Q1 or “No” to Q1 and in the past 12 months? Q2 Q2 [reported at age 11 and 14] Q2: Has he/she ever had hay fever? [reported at age 11 and 14] 23andMe 4,230 10,842 Have you ever had allergic “Yes” “No” rhinitis (stuffed or dripping nose caused by allergies)? Total 6,685 14,091 Table E3. Breakdown of samples used for each set of analyses performed.

AAGC 23andMe Asthma Asthma Total Total Hayfever + - ? Hayfever + - ? + 1505 317 0 1822 + 4230 8046 293 12569 - 717 632 0 1349 - 1138 10842 142 12122 ? 395 891 0 1286 ? 51 151 0 202 Total 2617 1840 0 4457 Total 5419 19039 435 24893

ALSPAC Raine Asthma Asthma Total Total Hayfever + - ? Hayfever + - ? + 668 806 234 1708 + 282 132 0 414 - 554 2132 247 2933 - 367 485 0 852 ? 351 321 0 672 ? 5 4 0 9 Total 1573 3259 481 5313 Total 654 621 0 1275

Samples included for analyses Analysis Cases Controls Results tables Asthma (A) A+H+ vs A-H- a e 1,E5,E6,E13,E14 Hayfever a+d+ + - ? A+ vs A- b+e+h E8 (H) g + a b c H+ vs H- a+b+c d+e+f E8 a+d+ E8 - d e f A+ vs A-H- e g ? g h i H+ vs A-H- a+b+c e E8 A+H- vs A-H- d e E8 A-H+ vs A-H- b e E8 A+H- vs A-H+ d b E9 Table E4. Participants included in the replication stage.

Cases Controls

Sex, Age Sex, Cohort Age, mean Asthma age-of-onset, N (%) N N N female (mean, range) N female (%) (range) (%) ≤16 >16 Unknown 23andMe 878 47 (13 - 95) 535 (61) 480 (55) 398 (45) 0 (0) 2,455 46 (13 - 91) 941 (38) Table E5. Association results for the top 11 variants obtained after applying genomic control

(GC) to the observed SNP association test statistics.

Risk allele GC-corrected† Nearest gene, Position, bp SNP, risk allele frequency, OR (95% CI) association .Chr kb distance range P-value 6 32734579 HLA-DQB1,1 rs9273373,G 0.54-0.58 1.24 (1.17-1.31) 2 x 10-13 4 38476105 TLR1,2* rs4833095,T 0.74-0.76 1.20 (1.14-1.26) 2 x 10-11 5 110495398 WDR36,1 rs1438673,C 0.49-0.52 1.16 (1.11-1.21) 9 x 10-11 2 102332981 IL1RL1,2* rs10197862,A 0.85-0.86 1.24 (1.16-1.32) 1 x 10-10 11 75976842 LRRC32,69 rs2155219,T 0.48-0.52 1.16 (1.11-1.21) 2 x 10-10 17 35376206 GSDMA,3* rs7212938,G 0.46-0.48 1.16 (1.10-1.21) 9 x 10-10 5 110429771 TSLP,6 rs1837253,C 0.71-0.75 1.17 (1.11-1.24) 3 x 10-9 9 6165855 IL33,40 rs72699186,T 0.15-0.16 1.26 (1.17-1.36) 5 x 10-9 8 81454434 ZBTB10,106 rs7009110,T 0.36-0.41 1.14 (1.09-1.20) 8 x 10-9 15 65255339 SMAD3,19* rs17294280,G 0.23-0.27 1.18 (1.12-1.25) 1 x 10-8 16 11136213 CLEC16A,47* rs62026376,C 0.72-0.74 1.17 (1.11-1.24) 3 x 10-8 * SNP is located within reported gene.

†We first adjusted the SE of the beta estimated for each SNP (N = 4,972,397) tested in the 23andMe study for the observed genome-wide λ of 1.055. The AAGC, ALSPAC and Raine GWAS had λ ~ 1 (Fig. E1) and so no GC correction was applied to these three studies. Next, we performed the meta-analysis of the 23andMe GC- corrected GWAS results with the original GWAS from the AAGC, ALSPAC and Raine, as described in the main text; this meta-analysis had a λ of 1.016. Lastly, we applied GC correction to the meta-analysis, resulting in a final λ of 1.00. All analyses performed with METAL. Table E6. Association results for rs7009110 and rs62026376 in the four individual cohorts.

ZBTB10 (rs7009110, A) CLEC16A (rs62026376, C) * Cohort N cases N controls OR SE P-value OR SE P-value AAGC 1,505 632 1.199 0.069 0.0086 1.200 0.076 0.0164 23andMe 4,230 10,842 1.127 0.027 7 x 10-6 1.171 0.030 2 x 10-7 ALSPAC 668 2,132 1.185 0.065 0.0089 1.025 0.073 0.7326 Raine 282 485 1.169 0.108 0.1471 1.088 0.123 0.5032

*The sentinel SNP for CLEC16A (rs62026376, meta-analysis P = 1 x 10-8) was not available in the ALSPAC and Raine studies. Instead, the results shown in this table for those two studies correspond to the proxy SNP (r2 = 0.95) rs12935657 (G is the predisposing allele, in phase with rs62026376:C). For rs12935657, the four-study meta-analysis P-value was 5 x 10-8. Table E7. Association with the ZBTB10 locus in eczema-free people.

ZBTB10 (rs7009110, T) Cohort N cases N controls OR SE P-value AAGC 674 607 1.172 0.082 0.0520 23andMe 2,648 9,955 1.159 0.032 4 x 10-6 ALSPAC 96 821 1.078 0.161 0.64 Raine 150 373 1.259 0.136 0.0914 Overall 3,568 11,756 1.162 0.029 1 x 10-7 Table E8. Association analyses of the top 11 variants stratified by asthma and hayfever status.

A+ (N=10,263) H+ (N=16,513) H+ (N=16,513) A+H- (N=2,776) A-H+ (N=9,301) SNP†, risk Locus vs vs vs vs vs allele A- (N=24,759) H- (N=17,256) A-H- (N=14,091) A-H- (N=14,091) A-H- (N=14,091) OR, 95% CI P-value OR, 95% CI P-value OR, 95% CI P-value OR, 95% CI P-value OR, 95% CI P-value OR, 95% CI P-value

-12 1.12 (1.08- -08 -14 1.13 (1.09- -09 HLA-DQB1 rs9273373,G 1.17 (1.12-1.22) 5x10 1.16) 2x10 1.22 (1.16-1.28) 2x10 1.18) 2x10 1.14 (1.05-1.25) 0.0019 1.07 (1.02-1.12) 0.0052 -07 1.14 (1.10- -12 -10 1.15 (1.10- -12 TLR1 rs4833095,T 1.11 (1.06-1.15) 7x10 1.18) 4x10 1.15 (1.10-1.21) 5x10 1.19) 2x10 1.08 (1.00-1.16) 0.0467 1.11 (1.07-1.16) 0.0000 -09 1.09 (1.06- -08 -11 1.11 (1.07- -10 WDR36 rs1438673,C 1.11 (1.07-1.15) 2x10 1.12) 7x10 1.14 (1.09-1.18) 7x10 1.14) 9x10 1.10 (1.04-1.17) 0.0022 1.07 (1.03-1.11) 0.0002 -09 1.14 (1.10- -09 -11 1.16 (1.10- -10 IL1RL1 rs10197862,A 1.16 (1.11-1.22) 3x10 1.20) 2x10 1.21 (1.15-1.28) 2x10 1.21) 8x10 1.13 (1.03-1.23) 0.0086 1.11 (1.05-1.17) 0.0001 -06 1.10 (1.07- -10 -07 1.11 (1.07- -10 LRRC32 rs2155219,T 1.08 (1.05-1.12) 4x10 1.14) 7x10 1.11 (1.07-1.15) 2x10 1.14) 5x10 1.00 (0.94-1.06) 0.9839 1.08 (1.04-1.12) 0.0001 -15 1.05 (1.02- -12 1.07 (1.03- GSDMA rs7212938,G 1.15 (1.11-1.19) 8x10 1.09) 0.0020 1.16 (1.11-1.20) 2x10 1.11) 0.0002 1.14 (1.07-1.22) 0.0001 1.02 (0.98-1.06) 0.3297 -11 1.06 (1.02- -10 1.08 (1.04- -05 TSLP rs1837253,C 1.15 (1.10-1.19) 4x10 1.10) 0.0022 1.16 (1.11-1.21) 3x10 1.12) 9x10 1.15 (1.07-1.24) 0.0002 1.03 (0.98-1.07) 0.2448 -08 1.13 (1.07- -05 -10 1.15 (1.08- -06 IL33 rs72699186,T 1.19 (1.12-1.26) 1x10 1.19) 2x10 1.25 (1.16-1.34) 5x10 1.22) 2x10 1.19 (1.06-1.34) 0.0028 1.08 (1.02-1.16) 0.0163 -06 1.08 (1.04- -06 -06 1.09 (1.05- -06 ZBTB10 rs7009110,T 1.09 (1.05-1.13) 5x10 1.11) 5x10 1.11 (1.06-1.15) 1x10 1.13) 1x10 1.05 (0.99-1.12) 0.1054 1.06 (1.02-1.10) 0.0049 -07 1.08 (1.04- -06 1.08 (1.04- SMAD3 rs17294280,G 1.12 (1.07-1.17) 3x10 1.12) 0.0002 1.13 (1.08-1.19) 1x10 1.13) 0.0002 1.06 (0.98-1.14) 0.1468 1.03 (0.98-1.08) 0.2882 -07 1.09 (1.04- -05 -08 1.10 (1.05- -06 CLEC16A rs62026376,C 1.12(1.07-1.17) 7x10 1.13) 3x10 1.15 (1.09-1.21) 4x10 1.14) 6x10 1.08 (0.99-1.17) 0.0957 1.05 (1.01-1.10) 0.0227

† SNPs rs9273373, rs72699186 and rs62026376 were not tested in ALSPAC and Raine, and so results for these three SNPs are based on the AAGC and 23andMe studies. The sample sizes for these three SNPs are N=8,036 for A+, N=20,879 for A-, N=14,391 for H+, N=13,471 for H-, N=11,474 for A-H-, N=1,855 for A+H- and N=8,363 for A-H+. Table E9. Comparison of risk allele frequency for the top 11 variants between individuals with asthma but not hayfever (A+H-, coded as cases) and individuals with hayfever but not asthma (A-H+, coded as controls).

A+H- (N=2,409) vs Locus SNP†, risk allele A-H+ (N=9,169) OR, 95% CI P-value HLA-DQB1 rs9273373,G 1.09 (1.00-1.20) 0.0560 TLR1 rs4833095,T 0.95 (0.87-1.03) 0.2362 WDR36 rs1438673,C 1.02 (0.95-1.10) 0.5341 IL1RL1 rs10197862,A 1.01 (0.91-1.12) 0.8552 LRRC32 rs2155219,T 0.92 (0.85-0.98) 0.0139 GSDMA rs7212938,G 1.15 (1.07-1.24) 0.0003 TSLP rs1837253,C 1.13 (1.04-1.23) 0.0037 IL33 rs72699186,T 1.07 (0.95-1.21) 0.2672 ZBTB10 rs7009110,T 0.97 (0.90-1.04) 0.3740 SMAD3 rs17294280,G 1.00 (0.92-1.10) 0.9329 CLEC16A rs62026376,C 1.02 (0.93-1.12) 0.6811

† The Raine study did not contribute to these analyses. SNPs rs9273373, rs72699186 and rs62026376 were not tested in ALSPAC and Raine, and so results for these three SNPs are based on the AAGC and 23andMe studies. The sample sizes for these three SNPs are N=1,855 for A+H- and N=8,363 for A-H+. Table E10. Association between the ZBTB10 and CLEC16A loci with asthma risk in the

GABRIEL GWASE14.

Sentinel SNP, Proxy SNP, Risk alleles in GABRIEL results Locus r2 risk allele risk allele phase? OR P-value ZBTB10 rs7009110, A rs6473226, T 0.96 yes 1.067 0.0029 ZBTB10 rs7009110, A rs1543857, G 0.97 yes 1.059 0.0085 CLEC16A rs62026376, C rs17673553, A 0.96 yes 1.073 0.0042 Table E11. Variants associated with variation in the numbers of peripheral blood leukocyte populationsE15 or lymphocyte subtypesE16 and in linkage disequilibrium (r2 > 0.3) with the

CLEC16A sentinel SNP.

Sentinel SNP Proxy SNP r2 Associated trait P-value rs62026376 rs9652582 0.43 Eosinophils 0.0029 rs62026376 rs3901386 0.42 CD56+ NK cells 0.0031 Table E12. Variants in the ZBTB10 or CLEC16A regions associated with other inflammatory or immune-related diseases and in linkage disequilibrium (r2 > 0.3) with the asthma-with- hayfever sentinel SNP.

Sentinel Locus Proxy SNP r2 Trait P-value Reference SNP ZBTB10 rs7009110 rs7000782 0.51 Atopic dermatitis 1x10-6 E17 CLEC16A rs62026376 rs12708716 0.55 Type 1 diabetes 3x10-18 E18 CLEC16A rs62026376 rs887864 0.53 Allergic rhinitis 1x10-6 E19 CLEC16A rs62026376 rs7200786 0.31 Multiple sclerosis 9x10-17 E20 Table E13. Nineteen variants associated with the risk of having asthma-with-hayfever at the suggestive significance level (3 x 10-8 < P ≤ 5 x 10-6).

Risk allele Heterogeneity test Position, Nearest gene, Association Chr SNP, risk allele frequency, OR (95% CI) P-value bp kb distance P-value range (I2, 95% CI) 10 6164627 RBM17,6 rs41295115,C 0.05-0.06 1.32 (1.19-1.47) 1 x 10-7 0.66 (0, 0-89) 5 110182208 SLC25A46,56 rs3853750,C 0.15-0.18 1.17 (1.10-1.23) 1 x 10-7 0.98 (0, 0-85) 12 60536687 FAM19A2,148* rs17605016,G 0.07-0.10 1.22 (1.14-1.32) 2 x 10-7 0.78 (0, 0-85) 9 6058077 RANBP6,52 rs343496,A 0.81-0.82 1.16 (1.10-1.23) 2 x 10-7 0.52 (0, 0-85) 17 35226234 IKZF3,48* rs12450323,T 0.17-0.19 1.17 (1.10-1.25) 5 x 10-7 0.78 (0, 0-89) 12 28086392 PTHLH,70 rs11049300,A 0.96-0.98 1.43 (1.24-1.65) 8 x 10-7 0.62 (0, 0-85) 21 36929813 SIM2,64 rs6517368,T 0.71-0.74 1.13 (1.08-1.19) 9 x 10-7 0.59 (0, 0-85) 8 10850884 XKR6,60* rs6982751,C 0.86-0.86 1.19 (1.11-1.28) 1 x 10-6 0.15 (50, 0-91) 2 218385831 TNS1,1* rs76043829,G 0.87-0.89 1.22 (1.13-1.33) 2 x 10-6 0.92 (0, 0-89) 14 102308211 TRAF3,5 rs8010932,A 0.81-0.84 1.15 (1.09-1.22) 3 x 10-6 0.20 (34, 0-77) 12 98119078 FAM71C,447 rs2712665,T 0.67-0.69 1.12 (1.07-1.17) 3 x 10-6 0.58 (0, 0-85) 6 148622701 SASH1,83 rs4896981,G 0.78-0.79 1.16 (1.09-1.23) 3 x 10-6 0.55 (0, 0-89) 1 108124058 VAV3,185* rs7521681,A 0.14-0.16 1.15 (1.08-1.22) 3 x 10-6 0.95 (0, 0-85) 1 197031259 PTPRC,38 rs2759643,A 0.67-0.72 1.12 (1.07-1.17) 3 x 10-6 0.37 (3, 0-85) 11 76047835 LRRC32,2* rs1320644,A 0.33-0.36 1.11 (1.06-1.17) 5 x 10-6 0.89 (0, 0-85) 11 120711564 SC5DL,22 rs2060009,A 0.68-0.72 1.12 (1.07-1.18) 5 x10-6 0.35 (7, 0-86) 1 67823218 GADD45A,100 rs787538,T 0.59-0.61 1.11 (1.06-1.16) 5 x 10-6 0.83 (0, 0-85) 5 83942985 EDIL3,227 rs72766477,A 0.04-0.04 1.34 (1.18-1.52) 5 x 10-6 0.73 (0, 0-89) 17 4958496 USP6,2 rs9912347,G 0.51-0.53 1.11 (1.06-1.16) 5 x 10-6 0.52 (0, 0-85) * SNP is located within reported gene. Table E14. Replication results for 19 variants associated with the risk of having asthma-with- hayfever at the suggestive significance level (5x10-8 < P ≤ 5x10-6) in the discovery analysis.

Replication analysis Discovery + replication Locus SNP Allele OR SE P-value OR SE P-value Direction SLC25A46 rs3853750 C 1.0562 0.0766 0.4764 1.1533 0.0276 2 x 10-7 -- PTHLH rs11049300 G 0.7624 0.1835 0.1322 0.7070 0.0674 3 x 10-7 ++ IKZF3 rs12450323 T 1.0883 0.0757 0.2652 1.1600 0.0292 4 x 10-7 ++ XKR6 rs6982751 G 0.8698 0.0861 0.1022 0.8419 0.0338 4 x 10-7 ++ RBM17 rs41295115 C 1.0563 0.1249 0.6623 1.2769 0.0486 5 x 10-7 -- EDIL3 rs72766477 A 1.3478 0.1437 0.0406 1.3402 0.0583 5 x 10-7 ++ TNS1 rs76043829 A 0.8684 0.0990 0.1503 0.8242 0.0388 6 x 10-7 -- VAV3 rs7521681 A 1.1452 0.0770 0.0799 1.1491 0.0280 7 x 10-7 ++ SIM2 rs6517368 C 0.9611 0.0644 0.5376 0.8928 0.0236 2 x 10-6 ++ FAM19A2 rs17605016 G 0.9793 0.0994 0.8328 1.1878 0.0359 2 x 10-6 -+ RANBP6 rs343496 T 1.0235 0.0721 0.7473 0.8810 0.0268 2 x 10-6 +- TRAF3 rs8010932 G 0.9937 0.0790 0.9362 0.8829 0.0283 1 x 10-5 ++ SASH1 rs4896981 A 1.0028 0.0711 0.9691 0.8858 0.0285 2 x 10-5 -+ FAM71C rs2712665 C 1.0433 0.0642 0.5100 0.9102 0.0227 3 x 10-5 +- GADD45 rs787538 C 1.0442 0.0585 0.4599 0.9189 0.0212 6 x 10-5 +- PTPRC rs2759643 T 1.0554 0.0605 0.3727 0.9142 0.0225 7 x 10-5 +- LRRC32 rs1320644 A 0.9511 0.0612 0.4115 1.0919 0.0222 7 x 10-5 +- SC5DL rs2060009 G 1.0734 0.0628 0.2603 0.9155 0.0230 1 x 10-4 +- USP6 rs9912347 A 1.0924 0.0576 0.1251 0.9262 0.0207 2 x 10-4 -+ E Figure Legends

Figure E1. Quantile-quantile (QQ) plots for the GWAS of asthma-with-hayfever in the four individual cohorts, as well as in the meta-analysis of all studies.

Figure E2. Main association results from the meta-analysis of four GWAS of asthma-with- hayfever.

Figure E3. Regional association results (-log10(P-value), y-axis) for chromosome 8q21. The most-associated SNP is shown in purple, and the color of the remaining markers reflects the linkage disequilibrium (r2) with the top SNP. The recombination rate (second y-axis) is plotted in light blue and is based on the CEU 1000G population. Plots were generated using

LocusZoom.E21

Figure E4. Regional association results (-log10(P-value), y-axis) for chromosome 16p13. The most-associated SNP is shown in purple, and the color of the remaining markers reflects the linkage disequilibrium (r2) with the top SNP. The recombination rate (second y-axis) is plotted in light blue and is based on the CEU 1000G population. Plots were generated using

LocusZoom.E21 Collaborators of the Australian Asthma Genetics Consortium (AAGC)

Dale R. Nyholta, John Beilbyb-d, Faang Cheahe, Desiree Mészárosf, Scott D. Gordona, Melissa

C. Southeyg, Margaret J. Wrighta, James Markosh, Li P. Chunge, Anjali K. Hendersa, Graham

Gilesi, Suzanna Templee, John Whitfielda, Brad Sheltone, Chalermchai Mitrpante, Mark

Jenkinsj, Haydn Waltersf

a Queensland Institute of Medical Research, Brisbane, Australia. b PathWest Laboratory Medicine of Western Australia (WA), Nedlands, Australia. c School of Pathology and Laboratory Medicine, The University of WA, Nedlands, Australia. d Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Perth,

Australia. e Lung Institute of WA and Centre for Asthma, Allergy and Respiratory Research, University of WA, Perth, Australia. f Menzies Research Institute, Hobart, Australia. g Department of Pathology, The University of Melbourne, Melbourne, Australia. h Launceston General Hospital, Lauceston, Australia. i Cancer Epidemiology Centre, The Cancer Council Victoria, Melbourne, Australia. j Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of

Melbourne, Melbourne, Australia.